Related papers: 2nd Place Report of MOSEv2 Challenge 2025: Concept Guided Video Object Segmentation via SeC

2nd Place Report of MOSEv2 Challenge 2025: Concept Guided Video Object Segmentation via SeC

URL: http://arxiv.org/abs/2509.23838v1
Date: Sun, 28 Sep 2025 12:26:03 GMT
Title: 2nd Place Report of MOSEv2 Challenge 2025: Concept Guided Video Object Segmentation via SeC
Authors: Zhixiong Zhang, Shuangrui Ding, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Jiaqi Wang,
Abstract summary: Semi-supervised Video Object aims to segment a specified target throughout a video sequence, by a first-frame mask.<n>SeC framework established a deep semantic understanding of the object for more persistent segmentation.<n>SeC achieved 39.7 JFn on the test set and ranked 2nd place in the Complex VOS track of the 7th Large-scale Video Object Challenge.
Score: 46.76209037655681
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Semi-supervised Video Object Segmentation aims to segment a specified target throughout a video sequence, initialized by a first-frame mask. Previous methods rely heavily on appearance-based pattern matching and thus exhibit limited robustness against challenges such as drastic visual changes, occlusions, and scene shifts. This failure is often attributed to a lack of high-level conceptual understanding of the target. The recently proposed Segment Concept (SeC) framework mitigated this limitation by using a Large Vision-Language Model (LVLM) to establish a deep semantic understanding of the object for more persistent segmentation. In this work, we evaluate its zero-shot performance on the challenging coMplex video Object SEgmentation v2 (MOSEv2) dataset. Without any fine-tuning on the training set, SeC achieved 39.7 \JFn on the test set and ranked 2nd place in the Complex VOS track of the 7th Large-scale Video Object Segmentation Challenge.

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